# TempTabQA: Temporal Question Answering for Semi-Structured Tables

Source: [TempTabQA: Temporal Question Answering for Semi-Structured Tables](<https://www.sota2.com/research/paper/68fa712f1e209131cea4fd49>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:00:40.638Z

Authors: Vivek Gupta, Pranshu Kandoi, Mahek Bhavesh Vora, Shuo Zhang, Yujie He, Ridho Reinanda, Vivek Srikumar

Published: 2023-11-14T08:57:01.000Z

## Abstract

Semi-structured data, such as Infobox tables, often include temporal information about entities, either implicitly or explicitly. Can current NLP systems reason about such information in semi-structured tables? To tackle this question, we introduce the task of temporal question answering on semi-structured tables. We present a dataset, TempTabQA, which comprises 11,454 question-answer pairs extracted from 1,208 Wikipedia Infobox tables spanning more than 90 distinct domains. Using this dataset, we evaluate several state-of-the-art models for temporal reasoning. We observe that even the top-performing LLMs lag behind human performance by more than 13.5 F1 points. Given these results, our dataset has the potential to serve as a challenging benchmark to improve the temporal reasoning capabilities of NLP models.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2311.08002>)

[PDF](<https://arxiv.org/pdf/2311.08002.pdf>)